Recent developments of Large Language Models (LLMs) show great potential in many areas, including software architecture. Although there is some work on applying LLMs during architecture design, using LLMs for adaptation of the architecture of a collective adaptive system at runtime has not yet been explored enough. In this paper, we explore two approaches for how LLMs can serve for architecture adaptation of collective adaptive systems based on autonomic component ensembles. The first approach employs an LLM during runtime as a part of the adaptation manager; the other one asks the LLM to generate it (in Python), which is then used for the ensemble formation (resolution) at runtime. The prompts for both approaches are automatically generated from an architectural specification, which includes constraints for the architecture. Based on experimental observations of two use cases, we show that LLMs are quite capable in online prompting in particular. Even without being explicitly provided with an adaptation strategy, an LLM can come up with an efficient heuristic for ensemble resolution and realize it. We show that the limiting factor for using LLMs this way is not time complexity (as would be the case when solving the problem as constraint optimization), but the “laziness” of LLMs when prompted with a larger problem instance, as also recently reported in other works. In addition, we map how the correctness of the LLM’s solution scales with different forms of prompting and problem size, which captures the effect of the LLMs’ laziness under different conditions.

Despite advancements in MLOps and AutoML, ML development still remains challenging for data scientists. First, there is poor support for and limited control over optimizing and evolving ML models. Second, there is lack of efficient mechanisms for continuous evolution of ML models which would leverage the knowledge gained in previous optimizations of the same or different models. We propose an experiment-driven MLOps approach which tackles these problems. Our approach relies on the concept of an experiment, which embodies a fully controllable optimization process. It introduces full traceability and repeatability to the optimization process, allows humans to be in full control of it, and enables continuous improvement of the ML system. Importantly, it also establishes knowledge, which is carried over and built across a series of experiments and allows for improving the efficiency of experimentation over time. We demonstrate our approach through its realization and application in the ExtremeXp11https://extremexp.eu/ project (Horizon Europe).

Machine Learning (ML) has advanced significantly, yet the development of ML workflows still relies heavily on expert intuition, limiting standardization. MLOps integrates ML workflows for reliability, while AutoML automates tasks like hyperparameter tuning. However, these approaches often overlook the iterative and experimental nature of the development of ML workflows. Within the ongoing ExtremeXP project (Horizon Europe), we propose an experiment-driven approach where systematic experimentation becomes central to ML workflow evolution. The framework created within the project supports transparent, reproducible, and adaptive experimentation through a formal metamodel and related domain-specific language. Key principles include traceable experiments for transparency, empowered decision-making for data scientists, and adaptive evolution through continuous feedback. In this paper, we present the framework from the model-based approach perspective. We discuss the lessons learned from the use of the metamodel-centric approach within the project—especially with use-case partners without prior modeling expertise.

Abstract Ensemble-based component systems have been used for many years to develop collective adaptive systems (CAS). The DEECo component model offers a framework for modeling and implementing ensemble-based component systems. Being expressive enough and having semantics specifically tailored towards dynamically evolving systems, DEECo has proven to be fairly powerful in modeling complex and dynamic architectures. We see great potential in employing large language models (LLMs) to simplify creating and refining the DEECo architectures. Since this constitutes a large research scope, in this paper, we focus on initial experiments to demonstrate how well generic LLMs (two OpenAI models executed remotely and four open-source models executed locally) understand the advanced concepts of ensemble-based CAS embodied in DEECo. We do so by systematically asking six questions about specific details of three DEECo applications that differ in the way they are specified. Our results indicate that LLMs can indeed understand ensemble-based architectures and show how this is influenced by the specification means. In particular, using external DSL, which is very self-explanatory, gave good results out of the box. Specifications embedded in existing programming languages needed a prior explanation of how to interpret them.

Systematic literature mapping is an essential part of research methodology. Conducting a systematic literature mapping is challenging. Researchers query publications from various sources, which need to be filtered, categorized, and cleared of duplicates. It is usually the case that the number of publications ranges between hundreds to thousands. The whole process is often performed iteratively and repeatedly, especially at the start of the mapping study, which only further increases the effort. When a team of researchers conducts a mapping study, the members may have different opinions on filtering and categorizing papers, which must be resolved. To our knowledge, this problem is very poorly supported by open-source tools. To address these issues, we present a tool called Robin which facilitates managing the steps of conducting a mapping study within a team. It provides search tools, categorization, and a platform for team members to define their criteria for including and excluding papers. In addition, Robin is connected to publicly available publications search platforms such as IEEE API and Scopus API. Robin is written in Python-Django and can be installed as a web application.

Modern software projects often incorporate some form of performance testing into their development cycle, intending to detect changes in performance between commits or releases. Performance testing generally relies on experimental evaluation using various benchmark workloads. To detect performance changes reliably, benchmarks must be executed many times to account for variability in the measurement results. While considered best practice, this approach can become prohibitively expensive when the number of versions and benchmark workloads increases. To alleviate the cost of performance testing, we propose an approach for the early stopping of non-productive experiments that are unlikely to detect a performance bug in a particular benchmark. The stopping conditions are based on benchmark-specific thresholds determined from historical data modified to emulate the potential effects of software changes on benchmark performance. We evaluate the approach on the GraalVM benchmarking project and show that it can eliminate about 50% of the experiments if we can afford to ignore about 15% of the least significant performance changes.

Computing systems form the backbone of many areas in our society, from manufacturing to traffic control, healthcare, and financial systems. When software plays a vital role in the design, construction, and operation, these systems are referred to as software-intensive systems. Self-adaptation equips a software-intensive system with a feedback loop that either automates tasks that otherwise need to be performed by human operators or deals with uncertain conditions. Such feedback loops have found their way to a variety of practical applications; typical examples are an elastic cloud to adapt computing resources and automated server management to respond quickly to business needs. To gain insight into the motivations for applying self-adaptation in practice, the problems solved using self-adaptation and how these problems are solved, and the difficulties and risks that industry faces in adopting self-adaptation, we performed a large-scale survey. We received 184 valid responses from practitioners spread over 21 countries. Based on the analysis of the survey data, we provide an empirically grounded overview the of state of the practice in the application of self-adaptation. From that, we derive insights for researchers to check their current research with industrial needs, and for practitioners to compare their current practice in applying self-adaptation. These insights also provide opportunities for applying self-adaptation in practice and pave the way for future industry-research collaborations.

In this position paper we formulate performance regression testing as an automated experimentation problem and focus on the problem of controlling the experiment so as to provide more computation time to experiments that are more likely to detect performance changes. Conversely, this requires detecting and stopping experiments early if they are unlikely to detect any performance changes. To this end, we present a method that uses results from previous performance testing experiments to predict the outcome of new experiments in early stages of their execution.

The high complexity, connectivity, and data exchange of modern software systems make it crucial to consider confidentiality early. An often used mechanism to ensure confidentiality is access control. When the system is modeled during design time, access control can already be analyzed. This enables early identification of confidentiality violations and the ability to analyze the impact of what-if scenarios. However, due to the abstract view of the design time model and the ambiguity in the early stages of development, uncertainties exist in the system environment. These uncertainties can have a direct effect on the validity of access control attributes in use, which might result in compromised confidentiality.To handle such known uncertainty, we present a notion of confidence in the context of design time access control. We define confidence as a composition of known uncertainties in the environment of the system, which influence the validity of access control attributes. We extend an existing modeling and analysis approach for design time access control with our notion of confidence. For evaluation, we apply the notion of confidence to multiple real-world case studies and discuss the resulting benefits for different stages of system development. We also analyze the expressiveness of the extended approach in defining confidentiality constraints and measure the accuracy in identifying confidentiality violations. Our results show that using the notion of confidence increases expressiveness while being able to accurately identify access control violations.

Self-adaptive systems comprise a complex domain of computing systems that are intensively studied but sparsely employed in real applications. Furthermore, recent trends in computer science are steering towards machine learning which has yet to fully penetrate this domain. We would like to present Simdex --- a realistic simulator of the self-adaptive backend that dispatches computing jobs among multiple workers. It is based on ReCodEx, a system for semi-automated evaluation of coding assignments that have been used for the past 5 years at our School of Computer Science. The simulator replays the workload logs recorded from ReCodEx over that period which provides a quite thorough evaluation and near-to-real feedback for the simulated scenarios. Furthermore, the design of the simulator is highly modular and allows the implementation of different self-adaptive controllers, including ones based on machine learning, as we demonstrate in our examples.

Self-adaptation equips a software system with a feedback loop that automates tasks that otherwise need to be performed by operators. Such feedback loops have found their way to a variety of practical applications, one typical example is an elastic cloud. Yet, the state of the practice in self-adaptation is currently not clear. To get insights into the use of self-adaptation in practice, we are running a large-scale survey with industry. This paper reports preliminary results based on survey data that we obtained from 113 practitioners spread over 16 countries, 62 of them work with concrete self-adaptive systems. We highlight the main insights obtained so far: motivations for self-adaptation, concrete use cases, and difficulties encountered when applying self-adaptation in practice. We conclude the paper with outlining our plans for the remainder of the study.

The paper presents a self-adaptive Kubernetes cloud controller for scheduling time-sensitive applications. The controller allows services to specify timing requirements (response time or throughput) and schedules services on shared cloud resources so as to meet the requirements. The controller builds and continuously updates an internal performance model of each service and uses it to determine the kind of resources needed by a service, as well as predict potential contention on shared resources, and (re-)deploys services accordingly. The controller is integrated with our highly-customizable data processing and visualization platform IVIS, which provides a web-based front-end for service deployment and visualization of results. The controller implementation is open-source and is intended to provide an easy-to-use testbed for experiments focusing on various aspects of adaptive scheduling and deployment in the cloud.

Cyber-physical systems (CPS) and IoT systems are nowadays commonly designed as self-adaptive, endowing them with the ability to dynamically reconFigure to reflect their changing environment. This adaptation concerns also the security, as one of the most important properties of these systems. Though the state of the art on adaptivity in terms of security related to these systems can often deal well with fully anticipated situations in the environment, it becomes a challenge to deal with situations that are not or only partially anticipated. This uncertainty is however omnipresent in these systems due to humans in the loop, open-endedness and only partial understanding of the processes happening in the environment. In this paper, we partially address this challenge by featuring an approach for tackling access control in face of partially unanticipated situations. We base our solution on special kind of aspects that build on existing access control system and create a second level of adaptation that addresses the partially unanticipated situations by modifying access control rules. The approach is based on our previous work where we have analyzed and classified uncertainty in security and trust in such systems and have outlined the idea of access-control related situational patterns. The aspects that we present in this paper serve as means for application-specific specialization of the situational patterns. We showcase our approach on a simplified but real-life example in the domain of Industry 4.0 that comes from one of our industrial projects.

This tool paper presents the IVIS platform for processing and visualizing IoT and CPS data. The platform provides a web-based interface that allows both definition of complex visualizations and data processing jobs as well as exploring the data. Compared to the existing open-source and commercial offerings, IVIS follows a different model and focuses on flexibility. Instead of providing a complex administrative UI for creating visualizations by dragging and dropping components onto a dashboard, IVIS provides a set of JavaScript-based visualization components that are glued together using simple JavaScript code. Similarly, the data processing jobs can be defined using code in scripting languages, such as Python, which allows exploiting the wealth of existing libraries for numerical processing. This not only makes the definition of visualizations and data processing jobs much more expressive, but it also turns out to be significantly easier to use when building complex parametric visualizations- especially when they need to deal with many sensors. This proved to be crucial in deploying IVIS in a number of international research projects, because it enabled us to rapidly setup complex visualizations and data-processing tasks, catering to project- and partner-specific requirements.

Cyber-physical systems (CPS) are complex, heterogeneous, and dynamic systems, spanning hardware and software components ranging from edge devices to cloud platforms. CPS need to satisfy many rigorous constraints, e.g., with respect to deadlines, safety, and quality, yielding a large configuration space where only a limited number of configurations meet the constraints and only a fraction are optimal regarding certain qualities. Finding the optimal configurations is hard, especially during runtime operation. We present QRML, the Quality and Resource Management domain-specific Language, and an accompanying toolset. QRML enables specifying heterogeneous hardware/software systems and their composition and configurations conveniently, automated reasoning about them, and generating implementation artifacts like quality and resource monitoring templates. A QRML model consists of a hierarchy of components. Component specifications express constraints and requirements, that may serve multiobjective quality and resource optimization and exploration purposes. The QRML toolset offers language support, visualizations, documentation generation, template-code generation, and constraint-solving support.

Abstract Smart system applications (SSAs)—a heterogeneous landscape of applications of Internet of things, cyber-physical systems, and smart sensing systems—are composed of autonomous yet inherently cooperating components. An important problem in this area is how to hoist the cooperation of software components forming dynamic groups—ensembles—at the architectural level of an SSA. This is hard since ensembles can overlap, be nested, and be dynamically formed and dismantled based on several criteria. A related problem is how to combine component and ensemble specification with a well-established language supported on multiple platforms. To target these problems, we propose a specification and implementation language Trait-based COmponent Ensemble Language (TCOEL) based on Scala internal DSL, to describe both the architecture and formation of dynamic ensembles of components and their functional internals. To raise the level of expressivity, we introduce the concept of domain-specific extensions (traits) to the TCOEL core to reflect different paradigms’ concerns—such as movement in a 2D map, state-space modeling of physical processes, and statistical reasoning about uncertainty. This allows for configuring TCOEL for the needs of a specific SSA use case and, at the same time, facilitates reuse. To evaluate TCOEL, we show how it can be beneficially used in addressing the coordination of agents in a RoboCup Rescue Simulation application.

As software architecture practice relies more and more on runtime data to inform decisions in continuous experimentation and self-adaptation, it is increasingly important to consider the quality of the data used as input to the different decision-making and prediction algorithms. One issue in data-driven decisions is that real-life data coming from running systems can contain invalid or wrong values which can bias the result of data analysis. Data-driven decision-making should therefore comprise detection and handling of data anomalies as an integral part of the process. However, currently, anomaly detection is either absent in runtime decision-making approaches for continuous experimentation and self-adaptation or difficult to tailor to domain-specific needs. In this paper, we contribute by proposing a framework that simplifies the detection of data anomalies in timeseries-outputs of running systems. The framework is generic, since it can be employed in different domains, and tunable, since it uses expert user input in tailoring anomaly detection to the needs and assumptions of each domain. We evaluate the feasibility of the framework by successfully applying it to detecting anomalies in a real-life timeseries dataset from the traffic domain.

More and more software-intensive systems employ machine learning and runtime optimization to improve their functionality by providing advanced features (e. g. personal driving assistants or recommendation engines). Such systems incorporate a number of smart software functions (SSFs) which gradually learn and adapt to the users' preferences. A key property of SSFs is their ability to learn based on data resulting from the interaction with the user (implicit and explicit feedback)-which we call trainability. Newly developed and enhanced features in a SSF must be evaluated based on their effect on the trainability of the system. Despite recent approaches for continuous deployment of machine learning systems, trainability evaluation is not yet part of continuous integration and deployment (CID) pipelines. In this paper, we describe the different facets of trainability for the development of SSFs. We also present our approach for automated trainability evaluation within an automotive CID framework which proposes to use automated quality gates for the continuous evaluation of machine learning models. The results from our indicative evaluation based on real data from eight BMW cars highlight the importance of continuous and rigorous trainability evaluation in the development of SSFs.

Mobile robots are increasingly used in our everyday life to autonomously realize missions. A variety of languages has been proposed to support roboticists in the systematic development of robotic applications, ranging from logical languages with well-defined semantics to domain-specific languages with user-friendly syntax. The characteristics of both of them have distinct advantages, however, developing a language that combines those advantages remains an elusive task. We present PROMISE, a novel language that enables domain experts to specify missions on a high level of abstraction for teams of autonomous robots in a user-friendly way, while having well-defined semantics. Our ambition is to permit users to specify high-level goals instead of a series of specific actions the robots should perform. The language contains a set of atomic tasks that can be executed by robots and a set of operators that allow the composition of these tasks in complex missions. The language is supported by a standalone tool that permits mission specification through a textual and a graphical interface and that can be integrated within a variety of frameworks. We integrated PROMISE with a software platform providing functionalities such as motion control and planning. We conducted experiments to evaluate the correctness of the specification and execution of complex robotic missions with both simulators and real robots. We also conducted two user studies to assess the simplicity of PROMISE. The results show that PROMISE effectively supports users to specify missions for robots in a user-friendly manner.

Modern cyber-physical systems (CPS) often involve distributed devices/components that closely interact with each other and their environment. In this context, operation conditions may constantly change and it is not always possible to guarantee quality of service (QoS), particularly, if resourcesdegrade or stop being available. In addition, sometimes, one would like QoS to scale up/down with operation conditions, e.g., maximize efficiency, minimize energy consumption, etc. without compromising safety. However, traditional design and development techniques fail to capture the dynamics of modern CPS, since they rather focus on individual components/devices, and are unable to provide such QoS guarantees. To overcome this problem, we propose a design methodology based on the concept of ensemble, i.e., a dynamic grouping of components, which allows for scalable QoS guaranties. We illustrate the utility of our approach based on a case study consisting of an intelligent production line and analyze the effect on performance as communication between components degrades. Finally, our methodology can be incorporated into existing ensemble-based tools such as DEECo, Helena or jRESP to generate executable code to be deployed onto distributed devices.

The Two-hemisphere model-driven (2HMD) approach assumes modelling and use of procedural and conceptual knowledge on an equal and related basis.This differentiates 2HMD approach from pure procedural, pure conceptual, and object oriented approaches.The approach may be applied in the context of modelling of a particular business domain as well as in the context of modelling the knowledge about the domain.Cyber-physical systems are heterogeneous systems, which require multi-disciplinary approach to their modelling.Modelling of cyber-physical systems by 2HMD approach gives an opportunity to transparently compose and analyse system components to be provided and components actually provided, and, thus, to identify and fill the gaps between desirable and actual system content.

The dynamic nature of complex Cyber-Physical Systems puts extra requirements on their functionalities: they not only need to be dependable, but also able to adapt to changing situations in their environment. When developing such systems, however, it is often impossible to explicitly design for all potential situations up front and provide corresponding strategies. Situations that come out of this “envelope of adaptability” can lead to problems that end up by applying an emergency fail-safe strategy to avoid complete system failure. The existing approaches to self-adaptation cannot typically cope with such situations better—while they are adaptive (and can apply learning) in choosing a strategy, they still rely on a pre-defined set of strategies not flexible enough to deal with those situations adequately. To alleviate this problem, we propose the concept of meta-adaptation strategies, which extends the limits of adaptability of a system by constructing new strategies at runtime to reflect the changes in the environment. Though the approach is generally applicable to most approaches to self-adaptation, we demonstrate our approach on IRM-SA—a design method and associated runtime model for self-adaptive distributed systems based on component ensembles. We exemplify the meta-adaptation strategies concept by providing three concrete meta-adaptation strategies and show its feasibility on an emergency coordination case study.

Smart Cyber-Physical Systems (sCPS) are complex distributed decentralized systems of cooperating components. They typically operate in uncertain environments and thus require means for managing variability at run-time. Architectural modes have traditionally been a proven means for the runtime variability. They are easy to understand, easy to realize in resource-constrained systems and (contrary to more sophisticated methods of learning) provide an explicit specification that can be inspected and validated at design time. However, in uncertain environments (which is the case of sCPS), they tend to lack expressivity to take into account the level of uncertainty and factor it in the mode-switching logic. In this paper we present a rich language to specify mode-switch guards. The semantics of the language is based on statistical tests, which, as we show, is a convenient way to reason about uncertainty in the state of the environment.

Cyber-physical system (CPS) have been recognized as a top-priority in research and development. The innovations sought for CPS demand them to deal effectively with dynamicity of their environment, to be scalable, adaptive, tolerant to threats, etc. -- i.e. they have to be smart. Although approaches in software engineering (SE) exist that individually meet these demands, their synergy to address the challenges of smart CPS (sCPS) in a holistic manner remains an open challenge. The workshop focuses on software engineering challenges for sCPS. The goals are to increase the understanding of problems of SE for sCPS, study foundational principles for engineering sCPS, and identify promising SE solutions for sCPS. Based on these goals, the workshop aims to formulate a research agenda for SE of sCPS.

Textual use-cases have been traditionally used in the initial stages of the software development process to describe software functionality from the user's perspective. Their advantage is that they can be easily understood by stakeholders and domain experts. However, since use-cases typically rely on natural language, they cannot be directly subject to a formal verification. In this article, we present a method (called Formal Verification of Annotated Use-Case Models, FOAM) for formal verification of use-cases. This method features simple user-definable annotations, which are inserted into a use-case to make its semantics more suitable for verification. Subsequently, a model-checking tool is employed to verify temporal invariants associated with the annotations. This way, FOAM allows harnessing the benefits of model checking while still keeping the use-cases understandable for non-experts.

Cyber-physical systems typically involve a large number of mobile autonomous devices that closely interact with each other and their environment. Standard design and development techniques from the embedded domain fail to accurately model the dynamics of such systems and, hence, there is an increasing need for new programming models and abstractions. Component-based design approaches are a promising solution to manage the complexity of large-scale dynamic systems. However, existing such approaches either do not accurately model transitory interactions between components -- which are typical of cyber-physical systems -- or do not provide guarantees for real-time behavior which is essential in many safety-critical applications. To overcome this problem, in this paper, we present a component-based design technique based on DEECo (Dependable Emergent Ensembles of Components). The DEECo framework allows modeling large-scale dynamic systems by a set of interacting components. In contrast to other component-based design approaches from the literature, DEECo provides mechanisms to describe transitory interactions between components. We introduce necessary extensions to the DEECo design flow and integrate it with real-time analysis techniques that allow reasoning about timing behavior at the component-description level. Finally, we illustrate the simplicity and usefulness of our approach on a case study consisting of an intelligent crossroad system.

Robot Soccer is a very attractive platform in terms of research. It contains a number of challenges in the areas of robot control, artificial intelligence and image analysis. This article presents a look at the overall architecture of the game and describes some results of our experiments in analysis and optimization of strategies using sequence extraction. We have extracted sequences of game situations from the log of a game played in our simulator, as they occurred during the game. Afterwards, these sequences were compared by methods LCS, LCSS and T-WLCS, which are usually used for sequence comparison in the sequence alignment area. Using these methods, we are able to visualize the relations between the sequences of game situations and clusters of similar game situations in a graph. In conclusion, a possible description improvement of these game situations is introduced. Therefore, a possible strategy improvement to ensure a smoother and faster performing of actions defined by these situations is described.

Cyber-physical systems (CPS) are systems of cooperating autonomous components which closely interact with and control the physical environment. Being distributed and typically based on periodic activities, CPS have to cope with the problem that data capturing a distributed state of the system and its environment are inherently inaccurate (they represent belief on the state). In particular, this poses a problem when dependability is being pursued. In this paper we address this issue by modeling belief at the architecture level. In particular, we enhance the architecture by models describing belief inaccuracy over time. We exploit these models to quantify at runtime the impact of belief staleness on its inaccuracy. We then use this quantification to drive architectural adaptation with the aim to increase dependability of the running CPS system.

Mobile cloud computing in the context of ad-hoc clouds brings new challenges when offloading computation from mobile devices. The management of application deployment needs to ensure that the offloading provides users with the expected benefits, but it suddenly needs to cope with a highly dynamic environment which lacks a central authority and in which computational nodes appear and disappear.

SUMMARY This paper has been motivated by experience gained with specification and code generation of control elements for a software component platform and general‐purpose programming language like Java and C. The problem to be addressed is two‐fold: first, several domain‐specific languages (DSL) are to be employed to express different element concerns (architecture, deployment context, code pattern) and second, porting to another general‐purpose language should avoid modification of the specification and related code generation process as much as possible. In both respects, the classical template‐based code generation technique proved to be inflexible, requiring the code generator to be blurred with ad hoc encoded DSL facets. The paper addresses the problem by introducing the concept of interoperable DSL family. Each member of the family is built around its core language, which can be further specialized by embedding into a target programming language. Interoperability of these DSLs is achieved at the level of abstract syntax trees (ASTs) with help of queries. As a proof of the concept, we have implemented the queries via the AST transformation rules of the Stratego/XT framework. In the evaluation, we provide a comparison with the original template‐based implementation, which clearly indicates the DSL family and AST transformation benefits. We also provide examples of application areas where the concept of interoperable DSL family can be employed (and also indicate how this can be accomplished). Copyright © 2012 John Wiley & Sons, Ltd.

Compared to functional unit testing, automated performance testing is difficult, partially because correctness criteria are more difficult to express for performance than for functionality. Where existing approaches rely on absolute bounds on the execution time, we aim to express assertions on code performance in relative, hardware-independent terms. To this end, we introduce Stochastic Performance Logic (SPL), which allows making statements about relative method performance. Since SPL interpretation is based on statistical tests applied to performance measurements, it allows (for a special class of formulas) calculating the minimum probability at which a particular SPL formula holds. We prove basic properties of the logic and present an algorithm for SAT-solver-guided evaluation of SPL formulas, which allows optimizing the number of performance measurements that need to be made. Finally, we propose integration of SPL formulas with Java code using higher-level performance annotations, for performance testing and documentation purposes.

As embedded and real-time systems became an inherent part of many electronic appliances of everyday use, the demand for their development has grown enormously. Increasing complexity of these systems leads to demands of tools and techniques addressing their efficient and short time-to-market development. One of the possible ways to tackle the problem is a reuse of tools, methodologies and know how already established and successfully adopted in other application domains. Such transfer cannot be done inherently without appropriate modification and methodical adaptation based on an analysis of domain requirements. The paper analyzes necessary modifications and extensions of a general purpose component-based technology to enable development of embedded real-time systems. In addition, we present our own experience obtained while tailoring advanced component framework SOFA 2 to support development of embedded and real-time systems.

In this paper we propose to use components for managing the increasing complexity in modern vehicular systems. Compared to other approaches, the distinguishing feature of our work is using and benefiting from components throughout the development process from early design to development and deployment, and an explicit separation of concerns at different levels of granularity. Based on the elaboration of the specifics of vehicular systems (resource constraints, real-time requirements, hard demands on reliability), the paper identifies concerns that need to be addressed by a component model for this domain, and describes a realization of such a component model.

Component-based development has become a recognized technique for building large scale distributed applications. Although the maturity of this technique, there appears to be quite a significant gap between (a) component systems that are rich in advanced features (e.g., component nesting, software connectors, versioning, dynamic architectures), but which have typically only poor or even no runtime support, and (b) component systems with a solid runtime support, but which typically possess only a limited set of the advanced features. In our opinion, this is mainly due to the difficulties that arise when trying to give proper semantics to the features and reify them in development tools and an runtime platform. In this paper, we describe the implementation of the runtime environment for the SOFA 2.0 component model. In particular, we focus on the runtime support of the advanced features mentioned above. The described issues and the solution are not specific only to SOFA 2.0, but they are general and applicable to any other component system aiming at addressing such features.

Component-based software engineering is a powerful paradigm for building large applications. However, our experience with building application of components is that the existing advanced component models (such as those offering component nesting, behavior specification and checking, dynamic reconfiguration to some extent, etc.) are subject to a lot of limitations and issues which prevent them from being accepted more widely (by industry in particular). We claim that these issues are specifically related to (a) the lack of support for dynamic reconfigurations of hierarchical architectures, (b) poor support for modeling and extendibility of the control part of a component, and (c) the lack of support for different communication styles applied in inter-component communication. In this paper, we show how these problems can be addressed and present an advanced component system SOFA 2.0 as a proof of the concept. This system is based on its predecessor SOFA, but it incorporates a number of enhancements and improvements

In most component models, a software component consists of a functional part and a controller part. The controller part may be extensible; however, existing component models provide no means to capture the structure of the controller part, and therefore neither to specify the controller part extensions. In this paper, we introduce a minimalist component model to capture the structure of the controller part, coining the term microcomponent for the controller part elements. We further introduce the concept of a component aspect as a consistent set of controller part extensions. Within this framework, it is possible to seamlessly integrate controller part extensions, applying them to the components selected in the application's launch configuration. We have evaluated these concepts in a prototype implementation.